BiGNoC: Accelerating Big Data Computing with Application-Specific Photonic Network-on-Chip Architectures Academic Article uri icon

abstract

  • © 2018 IEEE. In the era of big data, high performance data analytics applications are frequently executed on large-scale cluster architectures to accomplish massive data-parallel computations. Often, these applications involve iterative machine learning algorithms to extract information and make predictions from large data sets. Multicast data dissemination is one of the major performance bottlenecks for such data analytics applications in cluster computing, as terabytes of data need to be distributed frequently from a single data source to hundreds of computing nodes. To overcome this bottleneck for big data applications, we propose BiGNoC, a manycore chip platform with a novel application-specific photonic network-on-chip (PNoC) fabric. BiGNoC is designed for big data computing and exploits multicasting in photonic waveguides. For high performance data analytics applications, BiGNoC improves throughput by up to 9.9 × while reducing latency by up to 88 percent and energy-per-bit by up to 98 percent over two state-of-the-art PNoC architectures as well as a broadcast-optimized electrical mesh NoC architecture, and a traditional electrical mesh NoC architecture.

author list (cited authors)

  • Chittamuru, S., Dang, D., Pasricha, S., & Mahapatra, R.

citation count

  • 8

publication date

  • November 2018